EL-BERT at SemEval-2020 Task 10: A Multi-Embedding Ensemble Based Approach for Emphasis Selection in Visual Media

Chandresh Kanani, Sriparna Saha, Pushpak Bhattacharyya


Abstract
In visual media, text emphasis is the strengthening of words in a text to convey the intent of the author. Text emphasis in visual media is generally done by using different colors, backgrounds, or font for the text; it helps in conveying the actual meaning of the message to the readers. Emphasis selection is the task of choosing candidate words for emphasis, it helps in automatically designing posters and other media contents with written text. If we consider only the text and do not know the intent, then there can be multiple valid emphasis selections. We propose the use of ensembles for emphasis selection to improve over single emphasis selection models. We show that the use of multi-embedding helps in enhancing the results for base models. To show the efficacy of proposed approach we have also done a comparison of our results with state-of-the-art models.
Anthology ID:
2020.semeval-1.214
Volume:
Proceedings of the Fourteenth Workshop on Semantic Evaluation
Month:
December
Year:
2020
Address:
Barcelona (online)
Editors:
Aurelie Herbelot, Xiaodan Zhu, Alexis Palmer, Nathan Schneider, Jonathan May, Ekaterina Shutova
Venue:
SemEval
SIG:
SIGLEX
Publisher:
International Committee for Computational Linguistics
Note:
Pages:
1645–1651
Language:
URL:
https://aclanthology.org/2020.semeval-1.214
DOI:
10.18653/v1/2020.semeval-1.214
Bibkey:
Cite (ACL):
Chandresh Kanani, Sriparna Saha, and Pushpak Bhattacharyya. 2020. EL-BERT at SemEval-2020 Task 10: A Multi-Embedding Ensemble Based Approach for Emphasis Selection in Visual Media. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 1645–1651, Barcelona (online). International Committee for Computational Linguistics.
Cite (Informal):
EL-BERT at SemEval-2020 Task 10: A Multi-Embedding Ensemble Based Approach for Emphasis Selection in Visual Media (Kanani et al., SemEval 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.semeval-1.214.pdf